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Semantic Context MCP Server

@zishengwu

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

zishengwu

提交者

william cris

关于 Semantic Context MCP Server

A Model Context Protocol (MCP) server that leverages a vector database to efficiently index and query the codebase.

#semantic-context-mcp#code-search#vector-database

Semantic Context MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Semantic Context MCP Server": {
      "command": "fastmcp",
      "args": [
        "run",
        "your_code_base/semantic-context-mcp/vector_search/fast_mcp_server.py:mcp"
      ],
      "env": {
        "OPENAI_API_KEY": "your_api_key",
        "OPENAI_BASE_URL": "your_api_base_url",
        "OPENAI_MODEL_NAME": "your_embedding_model_name"
      }
    }
  }
}

Semantic Context MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Semantic Context MCP Server?

Semantic Context MCP Server is a Model Context Protocol (MCP) server that uses a vector database to index your codebase and perform semantic searches. It parses code into structural blocks (functions, classes), converts them to vector embeddings, and stores them locally, allowing you to find code snippets using natural language queries rather than keyword matching. The server runs in the background, automatically tracks file changes, and incrementally updates its index to keep context up-to-date.

How to use Semantic Context MCP Server?

Install by cloning the repository and setting environment variables for an OpenAI-compatible API (OPENAI_API_KEY, OPENAI_BASE_URL, OPENAI_MODEL_NAME). Configure the MCP JSON file in your IDE with the command fastmcp run pointing to the server script. The server provides three tools: full_index, status, and query. It automatically performs an initial full index and then runs periodic incremental updates every 5 minutes.

Key features of Semantic Context MCP Server

  • Incremental indexing using a Merkle Tree for efficient updates.
  • Multi-language AST parsing: Python, Java, C++, JavaScript, TypeScript, Go.
  • Semantic code search via natural language queries.
  • Background automation with periodic incremental updates every 5 minutes.
  • Local-first storage: all data kept in ~/.chromadb.

Use cases of Semantic Context MCP Server

  • Finding semantically relevant code snippets without manual keyword guessing.
  • Keeping code context automatically updated as you edit files.
  • Integrating semantic search into MCP-compatible IDEs.

常见问题

1

What dependencies are required?

Python 3.8+ and an OpenAI-compatible API for generating embeddings. You must set `OPENAI_API_KEY`, `OPENAI_BASE_URL`, and `OPENAI_MODEL_NAME`.

2

Where is the indexed data stored?

All data (vector database and index metadata) is stored locally in the user’s home directory under `~/.chromadb`.

3

How does incremental indexing work?

A Merkle Tree built from file content hashes detects changes. Only added, modified, or deleted files are re-parsed and re-indexed, making the process efficient.

4

Which languages are supported for code parsing?

AST parsing supports Python, Java, C++, JavaScript, TypeScript, and Go.

5

How often does the server update its index?

It performs a full index on startup, then runs periodic incremental updates every 5 minutes.

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